Forwarded from Python | Machine Learning | Coding | R
Please open Telegram to view this post
VIEW IN TELEGRAM
👍4❤1
Forwarded from Python | Machine Learning | Coding | R
ds full archive.pdf.pdf
55.2 MB
Best Data Science Archive Notes
✉️ Our Telegram channels: https://www.tgoop.com/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Please open Telegram to view this post
VIEW IN TELEGRAM
❤2👍1
🐍📰 Linear Algebra in Python: Matrix Inverses and Least Squares — https://realpython.com/python-linear-algebra/
#PythonProgramming #python
#PythonProgramming #python
✉️ Our Telegram channels: https://www.tgoop.com/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Please open Telegram to view this post
VIEW IN TELEGRAM
👍2
Forwarded from Python | Machine Learning | Coding | R
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://www.tgoop.com/addlist/8_rRW2scgfRhOTc0
✅ https://www.tgoop.com/Codeprogrammer
Please open Telegram to view this post
VIEW IN TELEGRAM
❤3👍2
Converting Pandas DataFrames to PyTorch DataLoaders for Custom Deep Learning Model Training
Link: https://machinelearningmastery.com/converting-pandas-dataframes-to-pytorch-dataloaders-for-custom-deep-learning-model-training/
Link: https://machinelearningmastery.com/converting-pandas-dataframes-to-pytorch-dataloaders-for-custom-deep-learning-model-training/
#PyTorch #Pandas #DataLoader #DeepLearning #MachineLearning #CustomModelTraining #PythonML #DataPreparation #AIWorkflow #MLPipeline #MachineLearningMastery
✉️ Our Telegram channels: https://www.tgoop.com/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Please open Telegram to view this post
VIEW IN TELEGRAM
❤2
Forwarded from Python | Machine Learning | Coding | R
Top 50 LLM Interview Questions!
A comprehensive resource that covers traditional ML basics, model architectures, real-world case studies, and theoretical foundations.
👇👇👇👇👇👇
A comprehensive resource that covers traditional ML basics, model architectures, real-world case studies, and theoretical foundations.
👇👇👇👇👇👇
✉️ Our Telegram channels: https://www.tgoop.com/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Please open Telegram to view this post
VIEW IN TELEGRAM
❤1👍1
Forwarded from Python | Machine Learning | Coding | R
LLM Interview Questions.pdf
71.2 KB
Top 50 LLM Interview Questions!
#LLM #AIInterviews #MachineLearning #DeepLearning #NLP #LLMInterviewPrep #ModelArchitectures #AITheory #TechInterviews #MLBasics #InterviewQuestions #LargeLanguageModels
✉️ Our Telegram channels: https://www.tgoop.com/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Please open Telegram to view this post
VIEW IN TELEGRAM
❤4👍2
Data Science Machine Learning Data Analysis Books pinned «Intent | AI-Enhanced Telegram 🌐 Supports real-time translation in 86 languages 💬 Simply swipe up during chat to let AI automatically generate contextual replies 🎙 Instant AI enhanced voice-to-text conversion 🧠 Built-in mainstream models including GPT-4o, Claude…»
𝗦𝘆𝘀𝘁𝗲𝗺_𝗗𝗲𝘀𝗶𝗴𝗻_𝗥𝗼𝗮𝗱𝗺𝗮𝗽_𝗳𝗼𝗿_𝗠𝗔𝗔𝗡𝗚_&_𝗕𝗲𝘆𝗼𝗻𝗱.pdf
12.5 MB
𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝗳𝗼𝗿 𝗠𝗔𝗔𝗡𝗚 & 𝗕𝗲𝘆𝗼𝗻𝗱 🚀
If you're targeting top product companies or leveling up your backend/system design skills, this is for you.
System Design is no longer optional in tech interviews. It’s a must-have.
From Netflix, Amazon, Uber, YouTube, Reddit, Inc., to Twitter, these case studies and topic breakdowns will help you build real-world architectural thinking.
📌 Save this post. Spend 40 mins/day. Stay consistent.
➊ 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗖𝗼𝗿𝗲 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀
👉 System Design Basics
🔗 https://bit.ly/3SuUR0Y)
👉 Horizontal & Vertical Scaling
🔗 https://bit.ly/3slq5xh)
👉 Load Balancing & Message Queues
🔗 https://bit.ly/3sp0FP4)
👉 HLD vs LLD, Hashing, Monolith vs Microservices
🔗 https://bit.ly/3DnEfEm)
👉 Caching, Indexing, Proxies
🔗 https://bit.ly/3SvyVDc)
👉 Networking, CDN, How Browsers Work
🔗 https://bit.ly/3TOHQRb
👉 DB Sharding, CAP Theorem, Schema Design
🔗 https://bit.ly/3CZtfLN
👉 Concurrency, OOP, API Layering
🔗 https://bit.ly/3sqQrhj
👉 Estimation, Performance Optimization
🔗 https://bit.ly/3z9dSPN
👉 MapReduce, Design Patterns
🔗 https://bit.ly/3zcsfmv
👉 SQL vs NoSQL, Cloud Architecture
🔗 https://bit.ly/3z8Aa49)
➋ 𝗠𝗼𝘀𝘁 𝗔𝘀𝗸𝗲𝗱 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀
🔗 https://bit.ly/3Dp40Ux
🔗 https://bit.ly/3E9oH7K
➌ 𝗖𝗮𝘀𝗲 𝗦𝘁𝘂𝗱𝘆 𝗗𝗲𝗲𝗽 𝗗𝗶𝘃𝗲𝘀 (𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗧𝗵𝗲𝘀𝗲!)
👉 Design Netflix
🔗 https://bit.ly/3GrAUG1
👉 Design Reddit
🔗 https://bit.ly/3OgGJrL
👉 Design Messenger
🔗 https://bit.ly/3DoAAXi
👉 Design Instagram
🔗 https://bit.ly/3BFeHlh
👉 Design Dropbox
🔗 https://bit.ly/3SnhncU
👉 Design YouTube
🔗 https://bit.ly/3dFyvvy
👉 Design Tinder
🔗 https://bit.ly/3Mcyj3X
👉 Design Yelp
🔗 https://bit.ly/3E7IgO5
👉 Design WhatsApp
🔗 https://bit.ly/3M2GOhP
👉 Design URL Shortener
🔗 https://bit.ly/3xP078x
👉 Design Amazon Prime Video
🔗https://bit.ly/3hVpWP4
👉 Design Twitter
🔗 https://bit.ly/3qIG9Ih
👉 Design Uber
🔗 https://bit.ly/3fyvnlT
👉 Design TikTok
🔗 https://bit.ly/3UUlKxP
👉 Design Facebook Newsfeed
🔗 https://bit.ly/3RldaW7
👉 Design Web Crawler
🔗 https://bit.ly/3DPZTBB
👉 Design API Rate Limiter
🔗 https://bit.ly/3BIVuh7
➍ 𝗙𝗶𝗻𝗮𝗹 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀
👉 All Solved Case Studies
🔗 https://bit.ly/3dCG1rc
👉 Design Terms & Terminology
🔗 https://bit.ly/3Om9d3H
👉 Complete Basics Series
🔗https://bit.ly/3rG1cfr
If you're targeting top product companies or leveling up your backend/system design skills, this is for you.
System Design is no longer optional in tech interviews. It’s a must-have.
From Netflix, Amazon, Uber, YouTube, Reddit, Inc., to Twitter, these case studies and topic breakdowns will help you build real-world architectural thinking.
📌 Save this post. Spend 40 mins/day. Stay consistent.
➊ 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗖𝗼𝗿𝗲 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀
👉 System Design Basics
🔗 https://bit.ly/3SuUR0Y)
👉 Horizontal & Vertical Scaling
🔗 https://bit.ly/3slq5xh)
👉 Load Balancing & Message Queues
🔗 https://bit.ly/3sp0FP4)
👉 HLD vs LLD, Hashing, Monolith vs Microservices
🔗 https://bit.ly/3DnEfEm)
👉 Caching, Indexing, Proxies
🔗 https://bit.ly/3SvyVDc)
👉 Networking, CDN, How Browsers Work
🔗 https://bit.ly/3TOHQRb
👉 DB Sharding, CAP Theorem, Schema Design
🔗 https://bit.ly/3CZtfLN
👉 Concurrency, OOP, API Layering
🔗 https://bit.ly/3sqQrhj
👉 Estimation, Performance Optimization
🔗 https://bit.ly/3z9dSPN
👉 MapReduce, Design Patterns
🔗 https://bit.ly/3zcsfmv
👉 SQL vs NoSQL, Cloud Architecture
🔗 https://bit.ly/3z8Aa49)
➋ 𝗠𝗼𝘀𝘁 𝗔𝘀𝗸𝗲𝗱 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀
🔗 https://bit.ly/3Dp40Ux
🔗 https://bit.ly/3E9oH7K
➌ 𝗖𝗮𝘀𝗲 𝗦𝘁𝘂𝗱𝘆 𝗗𝗲𝗲𝗽 𝗗𝗶𝘃𝗲𝘀 (𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗧𝗵𝗲𝘀𝗲!)
👉 Design Netflix
🔗 https://bit.ly/3GrAUG1
👉 Design Reddit
🔗 https://bit.ly/3OgGJrL
👉 Design Messenger
🔗 https://bit.ly/3DoAAXi
👉 Design Instagram
🔗 https://bit.ly/3BFeHlh
👉 Design Dropbox
🔗 https://bit.ly/3SnhncU
👉 Design YouTube
🔗 https://bit.ly/3dFyvvy
👉 Design Tinder
🔗 https://bit.ly/3Mcyj3X
👉 Design Yelp
🔗 https://bit.ly/3E7IgO5
👉 Design WhatsApp
🔗 https://bit.ly/3M2GOhP
👉 Design URL Shortener
🔗 https://bit.ly/3xP078x
👉 Design Amazon Prime Video
🔗https://bit.ly/3hVpWP4
👉 Design Twitter
🔗 https://bit.ly/3qIG9Ih
👉 Design Uber
🔗 https://bit.ly/3fyvnlT
👉 Design TikTok
🔗 https://bit.ly/3UUlKxP
👉 Design Facebook Newsfeed
🔗 https://bit.ly/3RldaW7
👉 Design Web Crawler
🔗 https://bit.ly/3DPZTBB
👉 Design API Rate Limiter
🔗 https://bit.ly/3BIVuh7
➍ 𝗙𝗶𝗻𝗮𝗹 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀
👉 All Solved Case Studies
🔗 https://bit.ly/3dCG1rc
👉 Design Terms & Terminology
🔗 https://bit.ly/3Om9d3H
👉 Complete Basics Series
🔗https://bit.ly/3rG1cfr
#SystemDesign #TechInterviews #MAANGPrep #BackendEngineering #ScalableSystems #HLD #LLD #SoftwareArchitecture #DesignCaseStudies #CloudArchitecture #DataEngineering #DesignPatterns #LoadBalancing #Microservices #DistributedSystems
✉️ Our Telegram channels: https://www.tgoop.com/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Please open Telegram to view this post
VIEW IN TELEGRAM
👍3❤1🔥1
🙏💸 500$ FOR THE FIRST 500 WHO JOIN THE CHANNEL! 🙏💸
Join our channel today for free! Tomorrow it will cost 500$!
https://www.tgoop.com/+Cl8uwGkD0l5lMGNl
You can join at this link! 👆👇
https://www.tgoop.com/+Cl8uwGkD0l5lMGNl
Join our channel today for free! Tomorrow it will cost 500$!
https://www.tgoop.com/+Cl8uwGkD0l5lMGNl
You can join at this link! 👆👇
https://www.tgoop.com/+Cl8uwGkD0l5lMGNl
❤2👍1
mcp guide.pdf.pdf
16.7 MB
A comprehensive PDF has been compiled that includes all MCP-related posts shared over the past six months.
(75 pages, 10+ projects & visual explainers)
Over the last half year, content has been published about the Modular Computation Protocol (MCP), which has gained significant interest and engagement from the AI community. In response to this enthusiasm, all tutorials have been gathered in one place, featuring:
* The fundamentals of MCP
* Explanations with visuals and code
* 11 hands-on projects for AI engineers
Projects included:
1. Build a 100% local MCP Client
2. MCP-powered Agentic RAG
3. MCP-powered Financial Analyst
4. MCP-powered Voice Agent
5. A Unified MCP Server
6. MCP-powered Shared Memory for Claude Desktop and Cursor
7. MCP-powered RAG over Complex Docs
8. MCP-powered Synthetic Data Generator
9. MCP-powered Deep Researcher
10. MCP-powered RAG over Videos
11. MCP-powered Audio Analysis Toolkit
(75 pages, 10+ projects & visual explainers)
Over the last half year, content has been published about the Modular Computation Protocol (MCP), which has gained significant interest and engagement from the AI community. In response to this enthusiasm, all tutorials have been gathered in one place, featuring:
* The fundamentals of MCP
* Explanations with visuals and code
* 11 hands-on projects for AI engineers
Projects included:
1. Build a 100% local MCP Client
2. MCP-powered Agentic RAG
3. MCP-powered Financial Analyst
4. MCP-powered Voice Agent
5. A Unified MCP Server
6. MCP-powered Shared Memory for Claude Desktop and Cursor
7. MCP-powered RAG over Complex Docs
8. MCP-powered Synthetic Data Generator
9. MCP-powered Deep Researcher
10. MCP-powered RAG over Videos
11. MCP-powered Audio Analysis Toolkit
#MCP #ModularComputationProtocol #AIProjects #DeepLearning #ArtificialIntelligence #RAG #VoiceAI #SyntheticData #AIAgents #AIResearch #TechWriting #OpenSourceAI #AI #python
✉️ Our Telegram channels: https://www.tgoop.com/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Please open Telegram to view this post
VIEW IN TELEGRAM
❤7👍1
Forwarded from Python | Machine Learning | Coding | R
10 GitHub repos to build a career in AI engineering:
(100% free step-by-step roadmap)
1️⃣ ML for Beginners by Microsoft
A 12-week project-based curriculum that teaches classical ML using Scikit-learn on real-world datasets.
Includes quizzes, lessons, and hands-on projects, with some videos.
GitHub repo → https://lnkd.in/dCxStbYv
2️⃣ AI for Beginners by Microsoft
This repo covers neural networks, NLP, CV, transformers, ethics & more. There are hands-on labs in PyTorch & TensorFlow using Jupyter.
Beginner-friendly, project-based, and full of real-world apps.
GitHub repo → https://lnkd.in/dwS5Jk9E
3️⃣ Neural Networks: Zero to Hero
Now that you’ve grasped the foundations of AI/ML, it’s time to dive deeper.
This repo by Andrej Karpathy builds modern deep learning systems from scratch, including GPTs.
GitHub repo → https://lnkd.in/dXAQWucq
4️⃣ DL Paper Implementations
So far, you have learned the fundamentals of AI, ML, and DL. Now study how the best architectures work.
This repo covers well-documented PyTorch implementations of 60+ research papers on Transformers, GANs, Diffusion models, etc.
GitHub repo → https://lnkd.in/dTrtDrvs
5️⃣ Made With ML
Now it’s time to learn how to go from notebooks to production.
Made With ML teaches you how to design, develop, deploy, and iterate on real-world ML systems using MLOps, CI/CD, and best practices.
GitHub repo → https://lnkd.in/dYyjjBGb
6️⃣ Hands-on LLMs
- You've built neural nets.
- You've explored GPTs and LLMs.
Now apply them. This is a visually rich repo that covers everything about LLMs, like tokenization, fine-tuning, RAG, etc.
GitHub repo → https://lnkd.in/dh2FwYFe
7️⃣ Advanced RAG Techniques
Hands-on LLMs will give you a good grasp of RAG systems. Now learn advanced RAG techniques.
This repo covers 30+ methods to make RAG systems faster, smarter, and accurate, like HyDE, GraphRAG, etc.
GitHub repo → https://lnkd.in/dBKxtX-D
8️⃣ AI Agents for Beginners by Microsoft
After diving into LLMs and mastering RAG, learn how to build AI agents.
This hands-on course covers building AI agents using frameworks like AutoGen.
GitHub repo → https://lnkd.in/dbFeuznE
9️⃣ Agents Towards Production
The above course will teach what AI agents are. Next, learn how to ship them.
This is a practical playbook for building agents covering memory, orchestration, deployment, security & more.
GitHub repo → https://lnkd.in/dcwmamSb
🔟 AI Engg. Hub
To truly master LLMs, RAG, and AI agents, you need projects.
This covers 70+ real-world examples, tutorials, and agent app you can build, adapt, and ship.
GitHub repo → https://lnkd.in/geMYm3b6
(100% free step-by-step roadmap)
A 12-week project-based curriculum that teaches classical ML using Scikit-learn on real-world datasets.
Includes quizzes, lessons, and hands-on projects, with some videos.
GitHub repo → https://lnkd.in/dCxStbYv
This repo covers neural networks, NLP, CV, transformers, ethics & more. There are hands-on labs in PyTorch & TensorFlow using Jupyter.
Beginner-friendly, project-based, and full of real-world apps.
GitHub repo → https://lnkd.in/dwS5Jk9E
Now that you’ve grasped the foundations of AI/ML, it’s time to dive deeper.
This repo by Andrej Karpathy builds modern deep learning systems from scratch, including GPTs.
GitHub repo → https://lnkd.in/dXAQWucq
So far, you have learned the fundamentals of AI, ML, and DL. Now study how the best architectures work.
This repo covers well-documented PyTorch implementations of 60+ research papers on Transformers, GANs, Diffusion models, etc.
GitHub repo → https://lnkd.in/dTrtDrvs
Now it’s time to learn how to go from notebooks to production.
Made With ML teaches you how to design, develop, deploy, and iterate on real-world ML systems using MLOps, CI/CD, and best practices.
GitHub repo → https://lnkd.in/dYyjjBGb
- You've built neural nets.
- You've explored GPTs and LLMs.
Now apply them. This is a visually rich repo that covers everything about LLMs, like tokenization, fine-tuning, RAG, etc.
GitHub repo → https://lnkd.in/dh2FwYFe
Hands-on LLMs will give you a good grasp of RAG systems. Now learn advanced RAG techniques.
This repo covers 30+ methods to make RAG systems faster, smarter, and accurate, like HyDE, GraphRAG, etc.
GitHub repo → https://lnkd.in/dBKxtX-D
After diving into LLMs and mastering RAG, learn how to build AI agents.
This hands-on course covers building AI agents using frameworks like AutoGen.
GitHub repo → https://lnkd.in/dbFeuznE
The above course will teach what AI agents are. Next, learn how to ship them.
This is a practical playbook for building agents covering memory, orchestration, deployment, security & more.
GitHub repo → https://lnkd.in/dcwmamSb
To truly master LLMs, RAG, and AI agents, you need projects.
This covers 70+ real-world examples, tutorials, and agent app you can build, adapt, and ship.
GitHub repo → https://lnkd.in/geMYm3b6
#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers
✉️ Our Telegram channels: https://www.tgoop.com/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Please open Telegram to view this post
VIEW IN TELEGRAM
❤3
This media is not supported in your browser
VIEW IN TELEGRAM
Over the last year, several articles have been written to help candidates prepare for data science technical interviews. These resources cover a wide range of topics including machine learning, SQL, programming, statistics, and probability.
1️⃣ Machine Learning (ML) Interview
Types of ML Q&A in Data Science Interview
https://shorturl.at/syN37
ML Interview Q&A for Data Scientists
https://shorturl.at/HVWY0
Crack the ML Coding Q&A
https://shorturl.at/CDW08
Deep Learning Interview Q&A
https://shorturl.at/lHPZ6
Top LLMs Interview Q&A
https://shorturl.at/wGRSZ
Top CV Interview Q&A [Part 1]
https://rb.gy/51jcfi
Part 2
https://rb.gy/hqgkbg
Part 3
https://rb.gy/5z87be
2️⃣ SQL Interview Preparation
13 SQL Statements for 90% of Data Science Tasks
https://rb.gy/dkdcl1
SQL Window Functions: Simplifying Complex Queries
https://t.ly/EwSlH
Ace the SQL Questions in the Technical Interview
https://lnkd.in/gNQbYMX9
Unlocking the Power of SQL: How to Ace Top N Problem Questions
https://lnkd.in/gvxVwb9n
How To Ace the SQL Ratio Problems
https://lnkd.in/g6JQqPNA
Cracking the SQL Window Function Coding Questions
https://lnkd.in/gk5u6hnE
SQL & Database Interview Q&A
https://lnkd.in/g75DsEfw
6 Free Resources for SQL Interview Preparation
https://lnkd.in/ghhiG79Q
3️⃣ Programming Questions
Foundations of Data Structures [Part 1]
https://lnkd.in/gX_ZcmRq
Part 2
https://lnkd.in/gATY4rTT
Top Important Python Questions [Conceptual]
https://lnkd.in/gJKaNww5
Top Important Python Questions [Data Cleaning and Preprocessing]
https://lnkd.in/g-pZBs3A
Top Important Python Questions [Machine & Deep Learning]
https://lnkd.in/gZwcceWN
Python Interview Q&A
https://lnkd.in/gcaXc_JE
5 Python Tips for Acing DS Coding Interview
https://lnkd.in/gsj_Hddd
4️⃣ Statistics
Mastering 5 Statistics Concepts to Boost Success
https://lnkd.in/gxEuHiG5
Mastering Hypothesis Testing for Interviews
https://lnkd.in/gSBbbmF8
Introduction to A/B Testing
https://lnkd.in/g35Jihw6
Statistics Interview Q&A for Data Scientists
https://lnkd.in/geHCCt6Q
5️⃣ Probability
15 Probability Concepts to Review [Part 1]
https://lnkd.in/g2rK2tQk
Part 2
https://lnkd.in/gQhXnKwJ
Probability Interview Q&A [Conceptual Questions]
https://lnkd.in/g5jyKqsp
Probability Interview Q&A [Mathematical Questions]
https://lnkd.in/gcWvPhVj
🔜 All links are available in the GitHub repository:
https://lnkd.in/djcgcKRT
Types of ML Q&A in Data Science Interview
https://shorturl.at/syN37
ML Interview Q&A for Data Scientists
https://shorturl.at/HVWY0
Crack the ML Coding Q&A
https://shorturl.at/CDW08
Deep Learning Interview Q&A
https://shorturl.at/lHPZ6
Top LLMs Interview Q&A
https://shorturl.at/wGRSZ
Top CV Interview Q&A [Part 1]
https://rb.gy/51jcfi
Part 2
https://rb.gy/hqgkbg
Part 3
https://rb.gy/5z87be
13 SQL Statements for 90% of Data Science Tasks
https://rb.gy/dkdcl1
SQL Window Functions: Simplifying Complex Queries
https://t.ly/EwSlH
Ace the SQL Questions in the Technical Interview
https://lnkd.in/gNQbYMX9
Unlocking the Power of SQL: How to Ace Top N Problem Questions
https://lnkd.in/gvxVwb9n
How To Ace the SQL Ratio Problems
https://lnkd.in/g6JQqPNA
Cracking the SQL Window Function Coding Questions
https://lnkd.in/gk5u6hnE
SQL & Database Interview Q&A
https://lnkd.in/g75DsEfw
6 Free Resources for SQL Interview Preparation
https://lnkd.in/ghhiG79Q
Foundations of Data Structures [Part 1]
https://lnkd.in/gX_ZcmRq
Part 2
https://lnkd.in/gATY4rTT
Top Important Python Questions [Conceptual]
https://lnkd.in/gJKaNww5
Top Important Python Questions [Data Cleaning and Preprocessing]
https://lnkd.in/g-pZBs3A
Top Important Python Questions [Machine & Deep Learning]
https://lnkd.in/gZwcceWN
Python Interview Q&A
https://lnkd.in/gcaXc_JE
5 Python Tips for Acing DS Coding Interview
https://lnkd.in/gsj_Hddd
Mastering 5 Statistics Concepts to Boost Success
https://lnkd.in/gxEuHiG5
Mastering Hypothesis Testing for Interviews
https://lnkd.in/gSBbbmF8
Introduction to A/B Testing
https://lnkd.in/g35Jihw6
Statistics Interview Q&A for Data Scientists
https://lnkd.in/geHCCt6Q
15 Probability Concepts to Review [Part 1]
https://lnkd.in/g2rK2tQk
Part 2
https://lnkd.in/gQhXnKwJ
Probability Interview Q&A [Conceptual Questions]
https://lnkd.in/g5jyKqsp
Probability Interview Q&A [Mathematical Questions]
https://lnkd.in/gcWvPhVj
https://lnkd.in/djcgcKRT
#DataScience #InterviewPrep #MachineLearning #SQL #Python #Statistics #Probability #CodingInterview #AIBootcamp #DeepLearning #LLMs #ComputerVision #GitHubResources #CareerInDataScience
✉️ Our Telegram channels: https://www.tgoop.com/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Please open Telegram to view this post
VIEW IN TELEGRAM
❤5
Transformer models have proven highly effective for many NLP tasks. While scaling up with larger dimensions and more layers can increase their power, this also significantly increases computational complexity. Mixture of Experts (MoE) architecture offers an elegant solution by introducing sparsity, allowing models to scale efficiently without proportional computational cost increases.
In this post, you will learn about Mixture of Experts architecture in transformer models. In particular, you will learn about:
Why MoE architecture is needed for efficient transformer scaling
How MoE works and its key components
How to implement MoE in transformer models
Let’s get started:
https://machinelearningmastery.com/mixture-of-experts-architecture-in-transformer-models/
In this post, you will learn about Mixture of Experts architecture in transformer models. In particular, you will learn about:
Why MoE architecture is needed for efficient transformer scaling
How MoE works and its key components
How to implement MoE in transformer models
Let’s get started:
https://machinelearningmastery.com/mixture-of-experts-architecture-in-transformer-models/
✉️ Our Telegram channels: https://www.tgoop.com/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Please open Telegram to view this post
VIEW IN TELEGRAM
❤4
Forwarded from Python | Machine Learning | Coding | R
Auto-Encoder & Backpropagation by hand ✍️ lecture video ~ 📺 https://byhand.ai/cv/10
It took me a few years to invent this method to show both forward and backward passes for a non-trivial case of a multi-layer perceptron over a batch of inputs, plus gradient descents over multiple epochs, while being able to hand calculate each step and code in Excel at the same time.
= Chapters =
• Encoder & Decoder (00:00)
• Equation (10:09)
• 4-2-4 AutoEncoder (16:38)
• 6-4-2-4-6 AutoEncoder (18:39)
• L2 Loss (20:49)
• L2 Loss Gradient (27:31)
• Backpropagation (30:12)
• Implement Backpropagation (39:00)
• Gradient Descent (44:30)
• Summary (51:39)
✉️ Our Telegram channels: https://www.tgoop.com/addlist/0f6vfFbEMdAwODBk
It took me a few years to invent this method to show both forward and backward passes for a non-trivial case of a multi-layer perceptron over a batch of inputs, plus gradient descents over multiple epochs, while being able to hand calculate each step and code in Excel at the same time.
= Chapters =
• Encoder & Decoder (00:00)
• Equation (10:09)
• 4-2-4 AutoEncoder (16:38)
• 6-4-2-4-6 AutoEncoder (18:39)
• L2 Loss (20:49)
• L2 Loss Gradient (27:31)
• Backpropagation (30:12)
• Implement Backpropagation (39:00)
• Gradient Descent (44:30)
• Summary (51:39)
#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers
Please open Telegram to view this post
VIEW IN TELEGRAM
❤3
If you are doing regression modeling in Python for explanatory purposes, don't use scikit-learn - it's not set up for explanatory modeling. Use #statsmodels. It's set up much better for immediately showing you all the underlying parameters of your model and helping you interpret your results..
#analytics #peopleanalytics #datascience #rstats #python
#analytics #peopleanalytics #datascience #rstats #python
✉️ Our Telegram channels: https://www.tgoop.com/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Please open Telegram to view this post
VIEW IN TELEGRAM
❤6👍2
Please open Telegram to view this post
VIEW IN TELEGRAM
❤1
Mathematical Theory of Deep Learning.pdf
7.8 MB
Unlock the Secrets of #DeepLearning with Math!
Excited to share a free resource for all data science enthusiasts! "Mathematical Theory of Deep Learning" by Philipp Petersen and Jakob Zech is now available on #arXiv.
This book breaks down the core pillars of deep learning with rigorous yet accessible #math. Perfect for grad students, researchers, or anyone curious about why neural networks work so well!
Key Takeaways:
Mastering feedforward neural networks and ReLU's expressive power
Exploring gradient descent, backpropagation, and the loss landscape
Unraveling generalization, double descent, and adversarial robustness.
Excited to share a free resource for all data science enthusiasts! "Mathematical Theory of Deep Learning" by Philipp Petersen and Jakob Zech is now available on #arXiv.
This book breaks down the core pillars of deep learning with rigorous yet accessible #math. Perfect for grad students, researchers, or anyone curious about why neural networks work so well!
Key Takeaways:
Mastering feedforward neural networks and ReLU's expressive power
Exploring gradient descent, backpropagation, and the loss landscape
Unraveling generalization, double descent, and adversarial robustness.
✉️ Our Telegram channels: https://www.tgoop.com/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Please open Telegram to view this post
VIEW IN TELEGRAM
❤4👍4
Forwarded from Python | Machine Learning | Coding | R
🔥 The coolest AI bot on Telegram
💢 Completely free and knows everything, from simple questions to complex problems.
☕️ Helps you with anything in the easiest and fastest way possible.
♨️ You can even choose girlfriend or boyfriend mode and chat as if you’re talking to a real person 😋
💵 Includes weekly and monthly airdrops!❗️
😵💫 Bot ID: @chatgpt_officialbot
💎 The best part is, even group admins can use it right inside their groups! ✨
📺 Try now:
• Type
• Type
• Type
Or just say
💢 Completely free and knows everything, from simple questions to complex problems.
☕️ Helps you with anything in the easiest and fastest way possible.
♨️ You can even choose girlfriend or boyfriend mode and chat as if you’re talking to a real person 😋
💵 Includes weekly and monthly airdrops!❗️
😵💫 Bot ID: @chatgpt_officialbot
💎 The best part is, even group admins can use it right inside their groups! ✨
📺 Try now:
• Type
FunFact!
for a jaw-dropping AI trivia.• Type
RecipePlease!
for a quick, tasty meal idea.• Type
JokeTime!
for an instant laugh.Or just say
Surprise me!
and I'll pick something awesome for you. 🤖✨Forwarded from Python | Machine Learning | Coding | R
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://www.tgoop.com/addlist/8_rRW2scgfRhOTc0
✅ https://www.tgoop.com/Codeprogrammer
Please open Telegram to view this post
VIEW IN TELEGRAM
❤1